Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloExperimenting WNN support in object tracking systems
Anno di pubblicazione2016
Formato
  • Elettronico
  • Cartaceo
Autore/iMassimo De Gregorio; Maurizio Giordano; Silvia Rossi; Mariacarla Staffa
Affiliazioni autoriIstituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" - CNR, Via Campi Flegrei 34, Pozzuoli, Italy; Istituto di Calcolo e Reti ad Alte Prestazioni - CNR, Via Pietro Castellino 111, Naples, Italy; Università degli Studi di Napoli "Federico II", Via Claudio 21 80125, Naples, Italy
Autori CNR e affiliazioni
  • MASSIMO DE GREGORIO
  • MAURIZIO GIORDANO
Lingua/e
  • inglese
AbstractObject tracking is a challenging problem in many computer vision applications, which go from robotics to surveillance systems. When applied to real world conditions, tracking methods found in the literature compete in solving some inherent difficulties of object segmentation and movement prediction, such as camouflage, occlusions, dynamic background, brightness, color and shape changes. To address some of these issues, we propose a general framework for object tracking by exploiting well-known segmentation techniques and a weightless neural network based prediction algorithm. The considered neural computing model is DRASiW, that we, here, extended with reinforcing and forgetting mechanisms. This model has the property of being noise tolerant and capable of learning step-by-step the new appearance of the moving object, by updating the learned object shape through the evolution of its internal representation (called "mental" image). The proposed object tracking framework has been evaluated on different benchmark videos. Experimental results show the viability and the benefits of the proposed DRASiW-based object tracking framework in the chosen case studies in comparison with three state-of-the-art methods. In addition, results provide useful insights about which combination of DRASiW-based operational modes and segmentation techniques improves the performance in the considered cases.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da79
Pagine a89
Pagine totali13
RivistaNeurocomputing (Amst.)
Attiva dal 1989
Editore: Elsevier Science Publishers - Amsterdam
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0925-2312
Titolo chiave: Neurocomputing (Amst.)
Titolo proprio: Neurocomputing. (Amst.)
Titolo abbreviato: Neurocomputing (Amst.)
Numero volume della rivista183
Fascicolo della rivista-
DOI10.1016/j.neucom.2015.09.117
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84958105934)
  • ISI Web of Science (WOS) (Codice:000371558800008)
Parole chiaveObject tracking, Weightless neural network
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ICAR — ICAR - Sede secondaria di Napoli
  • ISASI — Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello"
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD022.055.001 : Intelligenza Computazionale
Progetti Europei-
Allegati
Experimenting WNN support in object tracking systems (documento privato )
Descrizione: Reprint
Tipo documento: application/pdf